


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE     :					str      =       {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					int      =       [
     "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "IBertForMaskedLM",
     "IBertForMultipleChoice",
     "IBertForQuestionAnswering",
     "IBertForSequenceClassification",
     "IBertForTokenClassification",
     "IBertModel",
     "IBertPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ibert import (
      IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      IBertForMaskedLM,
      IBertForMultipleChoice,
      IBertForQuestionAnswering,
      IBertForSequenceClassification,
      IBertForTokenClassification,
      IBertModel,
      IBertPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




